The collection, guest-edited by the organizers of the Machine Learning in Structural Biology workshop at NeurIPS, showcases advanced computational methods that enhance our understanding of biological molecules.

Photo credit: Ellen Zhong

Editorial: Machine Learning for Structural Biology

Gabriele Corso, Gina El Nesr, and Hannah K. Wayment-Steele

PRX Life 1, 013011 – Published 28 August 2023

We are excited to partner with PRX Life to create this collection and showcase interdisciplinary research in an archival venue. Though wide in scope, these papers are all united in their use of machine learning to study the molecular underpinnings of life.

Guiding Diffusion Models for Antibody Sequence and Structure Co-design with Developability Properties

Amelia Villegas-Morcillo, Jana M. Weber, and Marcel J. T. Reinders

PRX Life 1, 013011 – Published 28 August 2023

A new approach for antibody design uses generative diffusion models to guide the discovery of complementarity-determining regions with properties suitable for therapeutic applications.

Conditioned Protein Structure Prediction

Tengyu Xie, Zilin Song, and Jing Huang

AFEXplorer tailors AlphaFold’s predictions by incorporating user-defined constraints to generate alternative protein conformations that match specific functional states – a tool for exploring dynamic protein structures, such as kinase activation states and membrane transporter configurations.

Protein Design by Integrating Machine Learning and Quantum-Encoded Optimization

Veronica Panizza et al.

Combining machine learning for structure prediction with quantum-inspired optimization for sequence selection, a new algorithm enables efficient and stable protein design.

Jointly Embedding Protein Structures and Sequences through Residue Level Alignment

Foster Birnbaum et al.

Residue Level Alignment integrates protein sequence and structure information in a self-supervised model, improving speed and precision in predicting protein binding and structural stability.

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